def find_offset_parameter(self, x_values=None, data=None):
""" This method convolves the data with a Lorentzian and the finds the
offset which is supposed to be the most likely valy via a histogram.
Additional the smoothed data is returned
@param array x_values: x values
@param array data: value of each data point corresponding to
x values
@return int error: error code (0:OK, -1:error)
@return float array data_smooth: smoothed data
@return float offset: estimated offset
"""
# lorentzian filter
mod, params = self.make_lorentzian_model()
# Todo: exclude filter in seperate method to be used in other methods
if len(x_values) < 20.:
len_x = 5
elif len(x_values) >= 100.:
len_x = 10
else:
len_x = int(len(x_values)/10.)+1
lorentz = mod.eval(x=np.linspace(0, len_x, len_x), amplitude=1, offset=0.,
sigma=len_x/4., center=len_x/2.)
data_smooth = filters.convolve1d(data, lorentz/lorentz.sum(),
mode='constant', cval=data.max())
# finding most frequent value which is supposed to be the offset
hist = np.histogram(data_smooth, bins=10)
offset = (hist[1][hist[0].argmax()]+hist[1][hist[0].argmax()+1])/2.
return data_smooth, offset
############################################################################
# #
# Additional routines with gaussian-like filter #
# #
############################################################################
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